Prediction is crucial to the manufacturing industries and other industries as well, and is an important part in the analysis of mega data. Future events can be predicted through the analysis of historical data. Of the many prediction methods available in existing literature, the ensemble learning method is one of the supervised learning methods of the machine learning methods. According to the ensemble learning method, a result is obtained by integrating various prediction methods (that is, basic hypotheses) through a combination of weights. Normally, the result obtained by the ensemble learning method is more accurate than the result obtained by one single prediction method. However, in practical application, as the environment varies with the time, concept drifting phenomenon may occur, and the accuracy of the ensemble learning model created according to historical data will decrease. Under such circumstances, the prediction model must be re-trained or adjusted by use of newly created data to restore the prediction accuracy within a short period of time, lest the manufacturing cost or risk might increase due to the drip in prediction accuracy.
According to the prior art, the sample weight is positively proportional to the number of erroneous predictions generated under basic hypotheses, and a larger sample weight is assigned to the sample data generating erroneous predictions under more basic hypotheses. When the sample weight is assigned in this way, these samples are overemphasized, and the influences of other correctable sample data are sacrificed.